Hotspots_COVID Supplement II

总结 这一应用是为了应对了解流行病学影响的迫切需要， 美国新批准的儿童SARS-CoV-2疫苗的非均匀吸收。尽管 由于COVID-19导致的持续感染和住院，毫无疑问，接种疫苗 带来了显著的改善。随着最近批准为年轻人接种疫苗， 孩子们，我们现在有了一个控制COVID-19的真实的机会。然而，有几个因素可能 阻碍这一目标的实现：反对疫苗和其他政策，如掩蔽， 居住限制和检测（特别是在学校）、新的变种和免疫力减弱。因此，在本发明中， 在未来几个月里，即使是接种了疫苗的亚群，如老年人，风险也可能增加。 对这种增加的风险进行建模，并找到最脆弱的亚组是一个非常具有挑战性的问题。 对公共卫生影响很大的问题。 我们的项目将开发一个严格的数据和模型驱动的方法来识别和表征 最脆弱的亚组，以及减轻其风险的研究策略;这种方法将是 弗吉尼亚州的评估。团队成员一直在支持弗吉尼亚州卫生部（VDH） 自2019冠状病毒病大流行开始以来的应对措施，并拥有新的高分辨率数据集， 专业知识，通过这种合作。 我们将扩展一个详细的，基于年龄分层代理模型（ABM）的SARS-CoV-2传播， 通过整合有关疫苗犹豫、免疫接种和学校的详细数据集， 混合和干预。这种反弹道导弹的校准、模拟和分析是困难的， 由于其规模庞大。利用这种模式来确定最脆弱的人口， 大型模型在计算上是非常具有挑战性的，并且以前没有做过。一个创新的- 该项目的目标是将人工智能和高性能计算的工具与大型 针对这些问题的反弹道导弹系统。该项目将在很大程度上建立在开发的工具和模型的基础上 作为家长补助的一部分

Summary This application is in response to the urgent need to understand the epidemiological impact of non-uniform uptake of the newly approved SARS-CoV-2 vaccine for children in the US. Despite the continuing infections and hospitalizations due to COVID-19, there is no doubt that vaccination has led to significantly improved outcomes. With the recent approval of vaccinations for young children, we now have a real opportunity for controlling COVID-19. However, several factors could pose roadblocks towards this goal: opposition to the vaccine and other policies such as masking, occupancy limits and testing (especially in schools), new variants and waning immunity. As a result, the risk for even well vaccinated subgroups, such as seniors, could increase in the coming months. Modeling this increased risk, and finding the most vulnerable subgroups is a very challenging problem with high public health impact. Our project will develop a rigorous data and model driven approach to identify and characterize the most vulnerable subgroups, and study strategies for mitigating their risk; this approach will be evaluated for Virginia. Team members have been supporting the Virginia Dept of Health’s (VDH) COVID-19 response since the start of the pandemic, and have novel high resolution datasets and domain expertise through this collaboration. We will extend a detailed, age-stratified agent based model (ABM) for SARS-CoV-2 transmis- sion in Virginia by incorporating detailed datasets on vaccine hesitancy, immunization, and school mixing and interventions. The calibration, simulation and analysis of such an ABM is difficult in itself due to its large scale. Using such a model to identify the most vulnerable populations in such large models is computationally very challenging, and has not been done before. An innovative as- pect of the project is the combination of tools from AI and high performance computing with large scale ABMs for such problems. The project will build significantly on tools and models developed by team members as part of the parent grant.